MEME version 3.0 (Release date: 2002/04/02 00:11:59)
For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.sdsc.edu.
This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.sdsc.edu.
If you use this program in your research, please cite:
Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994.
DATAFILE= /home/max/var/seq/5noambigous.fa ALPHABET= ACGT Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ 1 1.0000 2000 2 1.0000 1962 3 1.0000 2000 4 1.0000 2000 5 1.0000 2000 6 1.0000 1908
This information can also be useful in the event you wish to report a problem with the MEME software. command: meme /home/max/var/seq/5noambigous.fa -dna model: mod= zoops nmotifs= 1 evt= inf object function= E-value of product of p-values width: minw= 8 maxw= 50 minic= 0.00 width: wg= 11 ws= 1 endgaps= yes nsites: minsites= 2 maxsites= 6 wnsites= 0.8 theta: prob= 1 spmap= uni spfuzz= 0.5 em: prior= dirichlet b= 0.01 maxiter= 50 distance= 1e-05 data: n= 11870 N= 6 strands: + sample: seed= 0 seqfrac= 1 Letter frequencies in dataset: A 0.305 C 0.184 G 0.196 T 0.316 Background letter frequencies (from dataset with add-one prior applied): A 0.305 C 0.184 G 0.196 T 0.316
BL MOTIF 1 width=15 seqs=4 5 ( 1869) CTCAACCGTCCATTC 1 4 ( 1822) CTGAACCGTCCATTC 1 3 ( 31) TTGAACCTTCCACTC 1 1 ( 1373) CTCCACCCACCAATC 1 //
log-odds matrix: alength= 4 w= 15 n= 11786 bayes= 11.5243 E= 1.2e+002 -865 203 -865 -34 -865 -865 -865 166 -865 144 135 -865 130 44 -865 -865 171 -865 -865 -865 -865 244 -865 -865 -865 244 -865 -865 -865 44 135 -34 -28 -865 -865 125 -865 244 -865 -865 -865 244 -865 -865 171 -865 -865 -865 -28 44 -865 66 -865 -865 -865 166 -865 244 -865 -865
letter-probability matrix: alength= 4 w= 15 n= 11786 E= 1.2e+002 0.000760 0.748588 0.000488 0.250164 0.000760 0.000458 0.000488 0.998294 0.000760 0.499212 0.499241 0.000788 0.748889 0.249835 0.000488 0.000788 0.998266 0.000458 0.000488 0.000788 0.000760 0.997965 0.000488 0.000788 0.000760 0.997965 0.000488 0.000788 0.000760 0.249835 0.499241 0.250164 0.250136 0.000458 0.000488 0.748917 0.000760 0.997965 0.000488 0.000788 0.000760 0.997965 0.000488 0.000788 0.998266 0.000458 0.000488 0.000788 0.250136 0.249835 0.000488 0.499541 0.000760 0.000458 0.000488 0.998294 0.000760 0.997965 0.000488 0.000788
Time 24.43 secs.
CPU: birnbaum
MOTIFS
For each motif that it discovers in the training set, MEME prints the following information:
Multilevel TTATGTGAACGACGTCACACT consensus AA T A G A GA AA sequence T C TT T
You can convert these blocks to PSSMs (position-specific scoring matrices), LOGOS (color representations of the motifs), phylogeny trees and search them against a database of other blocks by pasting everything from the "BL" line to the "//" line (inclusive) into the Multiple Alignment Processor. If you include the -print_fasta switch on the command line, MEME prints the motif sites in FASTA format instead of BLOCKS format.